مقایسهٔ روش‌ها

	رگرسیون خطی مقاوم	رگرسیون لسو
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1964–1987	1996
پدیدآور	Huber, P. J.; Rousseeuw, P. J.	Tibshirani, R.
نوع	Outlier-resistant supervised regression	Regularized linear regression (L1 penalty)
منبع بنیادین	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
نام‌های دیگر	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
مرتبط	5	4
خلاصه	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.